Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data

Most COVID-19 studies commonly report figures of the overall infection at a state- or county-level. This aggregation tends to miss out on fine details of virus propagation. In this paper, we analyze a high-resolution COVID-19 dataset in Cali, Colombia, that records the precise time and location of every confirmed case. We develop a non-stationary spatio-temporal point process equipped with a neural network-based kernel to capture the heterogeneous correlations among COVID-19 cases. The kernel is carefully crafted to enhance expressiveness while maintaining model interpretability. We also incorporate some exogenous influences imposed by city landmarks. Our approach outperforms the state-of-the-art in forecasting new COVID-19 cases with the capability to offer vital insights into the spatio-temporal interaction between individuals concerning the disease spread in a metropolis

Learning a Universal Human Prior for Dexterous Manipulation from Human Preference

Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands. Even in simulation with no sample constraints, scripting controllers is intractable due to high degrees of freedom, and manual reward engineering can also be hard and lead to non-realistic motions. Leveraging the recent progress on Reinforcement Learning from Human Feedback (RLHF), we propose a framework to learn a universal human prior using direct human preference feedback over videos, for efficiently tuning the RL policy on 20 dual-hand robot manipulation tasks in simulation, without a single human demonstration. One task-agnostic reward model is trained through iteratively generating diverse polices and collecting human preference over the trajectories; it is then applied for regularizing the behavior of polices in the fine-tuning stage. Our method empirically demonstrates more human-like behaviors on robot hands in diverse tasks including even unseen tasks, indicating its generalization capability

Comparison of the clinical characteristics and prognosis between clear cell carcinomas and high-grade serous ovarian carcinomas

Objectives: To compare the clinical characteristics and prognosis of women with clear cell versus high-grade serous ovarian carcinoma. Material and methods: Retrospective analysis of the clinical data of 50 cases patients with ovarian clear cell carcinoma (OCCC) and 103 cases with high-grade serous ovarian carcinoma (HGSOC), who were initially treated and completed standardized therapy in Affiliated Hospital of Qingdao University from January 2013 to December 2017. Results: There were significant differences in age, gravidity (G > 1), chief complaint, with ovarian endometriosis, tumor diameter, unilateral or bilateral, cystic and solid tumor, CA125, HE4, CA199, lactate dehydrogenase (LDH), and FIGO stage between the two groups. The differences in the prognosis between OCCC patients and HGSOC patients with early stage (FIGO I–II) were not statistically significant. The 5-year overall survival and progression-free survival of OCCC patients were significantly worse than those of HGSOC patients with advanced stage (FIGO III–IV) (p < 0.05). FIGO stage and non-R0 resection were independent risk factors affecting the prognosis of patients with ovarian clear cell carcinoma, screening by Cox regression analysis. FIGO stage, the lowest value of CA125, and non-R0 resection were independent risk factors affecting the prognosis of patients with high-grade serous ovarian cancer. Conclusions: The clinical characteristics and prognosis of OCCC are different from those of HGSOC. Ovarian clear cell carcinoma (OCCC) patients have a significantly worse prognosis than those with HGSOC in the advanced stage (FIGO Ⅲ–Ⅳ). Satisfactory tumor resection is an essential factor related to the prognosis of patients with OCCC and HGSOC

Droplet microfluidics on analysis of pathogenic microbes for wastewater-based epidemiology.

Infectious diseases caused by pathogenic microbes have posed a major health issue for the public, such as the ongoing COVID-19 global pandemic. In recent years, wastewater-based epidemiology (WBE) is emerging as an effective and unbiased method for monitoring public health. Despite its increasing importance, the advancement of WBE requires more competent and streamlined analytical platforms. Herein we discuss the interactions between WBE and droplet microfluidics, focusing on the analysis of pathogens in droplets, which is hard to be tackled by traditional analytical tools. We highlight research works from three aspects, namely, quantitation of pathogen biomarkers in droplets, single-cell analysis in droplets, and living cell biosensors in droplets, as well as providing future perspectives on the synergy between WBE and droplet microfluidics

Experimental Investigation on the Cyclic Shear Mechanical Characteristics and Dynamic Response of a Steel–Silt Interface in the Yellow River Delta

The shear behavior and dynamic response of a steel–silt interface are significant for the safety and stability of offshore structures in the Yellow River Delta. A series of steel–silt interface cyclic shear tests under constant normal load conditions (CNL) were carried out to explore the effects of normal stress, shear amplitude, roughness, and water content on the interface shear strength, shear stiffness, and damping ratio using a large interface shear apparatus. The preliminary results showed that the amplitude of normal stress and shear amplitude affected the interface’s shear strength, stiffness, and damping ratio in a dominant manner. The roughness and water content were also crucial factors impacting the rule of shear strength, shear stiffness, and damping ratio, changing with the number of cycles. Under various scenarios, the steel–silt interface weakened distinctively, and the energy dissipation tended to be asymptotic with the cyclic shear

Biological Uptake and Depuration of Radio-labeled Graphene by <i>Daphnia magna</i>

Graphene layers are potential candidates in a large number of applications. However, little is known about their ecotoxicological risks largely as a result of a lack of quantification techniques in complex environmental matrices. In this study, graphene was synthesized by means of graphitization and exfoliation of sandwich-like FePO₄/dodecylamine hybrid nanosheets, and ¹⁴C was incorporated in the synthesis. ¹⁴C-labeled graphene was spiked to artificial freshwater and the uptake and depuration of graphene by <i>Daphnia magna</i> were assessed. After exposure for 24 h to a 250 μg/L solution of graphene, the graphene concentration in the organism was nearly 1% of the organism dry mass. These organisms excreted the graphene to clean artificial freshwater and achieved roughly constant body burdens after 24 h depuration periods regardless of the initial graphene exposure concentration. Addition of algae and humic acid to water during the depuration period resulted in release of a significant fraction (>90%) of the accumulated graphene, but some still remained in the organism. Accumulated graphene in adult <i>Daphnia</i> was likely transferred to the neonates. The uptake and elimination results provided here support the environmental risk assessment of graphene and the graphene quantification method is a powerful tool for additional studies